Statistical data shows that position searching, for example, searching the geographical position of shopping malls, hotels, and schools, or finding all the nearest restaurants and cinemas around the user, is the major need when users using electronic maps. As the technology develops, the results of position searching are becoming richer and richer. Except the accurate place can be positioned, additional text information such as addresses, telephone numbers, and comments, and image information can also be provided. Due to the visual and intuitive features, images are becoming an important manner for showing position-related information. However, in the known art, the images are mainly uploaded by users or searched from the web, and thus, these images suffer from the following drawbacks.
1) These images have poor correlation to the searching object. For example, some images can't correctly show the position of the searching object, and evenly have none business of the searching object.
2) It is costly and of low efficiency to associate these images to correct positions. The images uploaded by users or searched from the web are full of randomness, and the data content is irregular. Thus, manual reviewing is necessary to associate these images to correct positions. Generally, the ratio of the approved images is very low.